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Models & Platforms

Fine-tuning

The process of further training a pre-trained model on a smaller, task-specific dataset to improve its performance on that particular task or domain.

Why it matters

Fine-tuning lets you customize foundation models for your exact use case. The rise of parameter-efficient methods like LoRA has made it accessible to teams without massive GPU budgets.

When to fine-tune

Fine-tuning makes sense when you need to teach a model a specific style, format, or domain vocabulary that prompt engineering alone can't capture. Common use cases include custom tone-of-voice, specialized classification, and structured output formatting.

Fine-tuning vs. RAG

A common misconception is that fine-tuning teaches a model new facts. In practice, RAG is better for knowledge (it can be updated without retraining) while fine-tuning is better for behavior (teaching the model how to respond, not what to say).

Techniques

  • Full fine-tuning — updates all model weights. Expensive but thorough.
  • LoRA / QLoRA — only trains small adapter layers. Much cheaper, often 90%+ of the quality.
  • RLHF — fine-tunes using human preference data to align model behavior.
  • DPO — Direct Preference Optimization, a simpler alternative to RLHF.

Related Terms

Large Language Model(LLM)— A neural network trained on massive text corpora that can understand and generate human language, typically with billions of parameters.Retrieval-Augmented Generation(RAG)— A technique that grounds a language model's output in external data by retrieving relevant documents before generating a response.Embeddings— Dense numerical representations of text (or other data) in a high-dimensional vector space, where similar meanings are placed closer together.

On the AI Radar

Embedding Fine-tuning— Adapting pre-trained embedding models to domain-specific data to improve retrieval accuracy in RAG pipelines and semantic search. Tooling has matured with Sentence Transformers v3 and Unsloth making the process accessible on consumer hardware.
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